plot.aspect_importance: Function for plotting aspect_importance results

In triplot: Explaining Correlated Features in Machine Learning Models

Description

This function plots the results of aspect_importance.

Usage

## S3 method for class 'aspect_importance'
plot(
  x,
  ...,
  bar_width = 10,
  show_features = aspects_on_axis,
  aspects_on_axis = TRUE,
  add_importance = FALSE,
  digits_to_round = 2,
  text_size = 3
)

Arguments

x	object of aspect_importance class
...	other parameters
bar_width	bar width
show_features	if TRUE, labels on axis Y show aspect names, otherwise they show features names
aspects_on_axis	alias for show_features held for backwards compatibility
add_importance	if TRUE, plot is annotated with values of aspects importance
digits_to_round	integer indicating the number of decimal places used for rounding values of aspects importance shown on the plot
text_size	size of labels annotating values of aspects importance, if applicable

Value

a ggplot2 object

Examples

library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

titanic_ai <- predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)
plot(titanic_ai)

triplot documentation built on July 13, 2020, 5:08 p.m.